The contour extraction of a moving object, especially of a deforming object, is a challenge in the field of computer vision. In actual applications, for example, in the medical field, the contour extraction of an organ or a part of an organ from a three-dimensional image time series acquired by a computed tomography (CT) apparatus, a Magnetic Resonance Imaging (MRI) apparatus, an ultrasonic (UL) apparatus and the like is beneficial to subsequent measurement on various parameters of the organ. However, the deforming motion of an object leads to a large variation in the orientation, size and shape of the object in an image time series and the image intensity, thus it is difficult to accurately extract the contours of the object in the respective images at different motion stages.
In addition, in the field of cardiology, a nuclear magnetic resonance imaging technology is typically used to provide a three-dimensional image time series (3D+T) of a heart. Doctors are highly interested in recognizing a ventricle, an endocardium, an epicardium. The contours of the recognized ventricle, endocardium and epicardium can be used to measure a ventricular blood volume (ejection fraction), the motion of a ventricular wall, a feature of wall thickness and the like at different stages of a cardiac cycle. The left ventricle (LV) is of great importance because it pumps oxygenated blood to various issues of a body from the heart.
In the prior art, models, some researchers have constructed models such as a four-dimensional (4D) probabilistic atlas of a heart and a three-dimensional (3D) LV surface model to aid left ventricle segmentation. Also some methods are studied to segment LV using an active shape by gradient, intensity and shape features. Certainly, more semi-automatic LV segmentation methods are studied which make use of user interaction. In recent years, more and more researchers have been devoted to the development of a fully-automatic LV segmentation method and made some achievements. For instance, Marie-Pierre Jolly and Ying Sun respectively proposed some methods (referring to US Patent Applications US2009/0232371 and US2009/0290777) as to the automatic segmentation of an LV.
The model-based methods provided in the prior art have difficulties in capturing variations beyond the training sets thereof. The commonly-used methods based on snake (dynamic contour model) algorithm are quite sensitive to noises and physical papillary muscle of the LV, and sometimes also sensitive to initial conditions. Most of the semi-automatic methods need the interaction of a user, which is subjective and time-consuming for the doctors. Some automatic methods have many assumptions on the shape and pixel brightness of the heart and need improvement in robustness.